AUC = 0.5 represents a test performed at chance for binary classification (i.e., the model performs no better than a coin flip), while AUC = 1 means a perfect test where all legitimate users succeed and adversaries failed to enter the system.
Therefore, under reasonable assumptions, that reversion back is most likely on binary splits, our total chance of setting m ik = 0 when the true value is m ik > 0, is on the same order of magnitude as twice the chance that the coalescent tree contains mutations that reverted back on the same link, which are inherently unobservable (Rosset et al., 2008).
Nonlinear fuel and carbon emission cost functions, chance constraints and binary aircraft assignment decisions make the problem significantly more difficult.
Figure 3 shows that this binary decoder, now operating with a chance level of 50%, still performed above chance with approximately the same time course as the 8-location decoder.
Phytoseiulus persimilis had higher survival chances than both other species, N. californicus had higher survival chances than A. andersoni (binary logistic regression, d.f. = 1: A. andersoni versus P. persimilis: Wald = 38.919, P < 0.001; A. andersoni versus N. californicus: Wald = 28.627, P < 0.001; P. persimilis versus N. californicus: Wald = 6.285, P = 0.012) (Fig. 1A, B, C).
Individual performance maps were corrected for chance level by subtracting 50% (binary discrimination) from every voxel.
In contrast to chance-constrained models requiring extra binary variables, risk constraints based on CVaR only involve linear constraints and continuous variables, making it more computationally attractive.
In addition, another binary covariate is generated, which has an equal chance of taking the value of 0 or 1 and is independent of the intervention and the outcome.
A triplet motif contains a mutually interacting set of genes, and therefore is less likely to arise by chance, or through experimental noise, than binary interactions, or sets of three genes arranged in a linear manner.
The monkey data were analyzed in a similar way as the human data, except that we collapsed multiple frequency conditions into a binary, low versus high, classification problem (with a chance level of 50%).
